Abstract
Cytological investigation is used for exploring cervical cancer. However, cytotechnologists are forced to investigate a few observed cells from many normal cervical cells. Thus, cytological investigation is cumbersome, and it takes long time. Therefore, the purpose of our study is to evaluate whether automated detection and classification of observed cells could be realized for reduction of the burden of cytotechnologists and the time cost of cytology using deep learning. We proposed a method to detect observed cells to be diagnosed by using Faster R-CNN, which is used for object detection. The method also classifies the grade of malignancy of the observed cells. We classified the grade of malignancy into three classes in consideration of the policy of treatment based on the Bethesda classification, which is used to diagnose cervical cancer cytology; normal group, medical follow-up group, surgical procedure needed group. We divided pathological data into training and test for five-hold cross-validation. We compared the result obtained from Faster R-CNN and the annotation by the cytotechnologist by F-measures. As a result, the normal group was 47.9±9.0 [％], while the medical follow-up group and the surgical procedure needed group were 75.0±8.6 [％] and 82.3±2.9 [％], respectively. The results obtained from the medical follow-up group and the surgical procedure needed group showed the effectiveness of our proposed method. However, the low performance of the normal group suggested the difficulty of annotation for all cervical cells.
